Kernelized Locality-Sensitive Hashing for Semi-Supervised Agglomerative Clustering
Machine Learning
2013-01-17 v1 Computer Vision and Pattern Recognition
Machine Learning
Abstract
Large scale agglomerative clustering is hindered by computational burdens. We propose a novel scheme where exact inter-instance distance calculation is replaced by the Hamming distance between Kernelized Locality-Sensitive Hashing (KLSH) hashed values. This results in a method that drastically decreases computation time. Additionally, we take advantage of certain labeled data points via distance metric learning to achieve a competitive precision and recall comparing to K-Means but in much less computation time.
Keywords
Cite
@article{arxiv.1301.3575,
title = {Kernelized Locality-Sensitive Hashing for Semi-Supervised Agglomerative Clustering},
author = {Boyi Xie and Shuheng Zheng},
journal= {arXiv preprint arXiv:1301.3575},
year = {2013}
}